Expert: AI Tools Sidelining Bedside Nurse Input
A recent essay argues that most clinical AI tools are designed with physicians as the primary users, often ignoring crucial input from frontline nurses. The author cites a nurse whose concerns about patient deterioration were discounted until an AI sepsis alert confirmed her assessment. This highlights a trend where AI solutions are built without sufficient input from nurses who perform real-time, bedside assessments.
- To bridge the gap between clinical practice and IT, nurse informaticists often translate clinician needs into "user stories" for technical teams, ensuring new technology supports nursing workflows and improves patient safety. ICU experience is valuable in this role for understanding the nuances of data capture in high-acuity settings, which influences how data is processed and analyzed. - The American Nurses Credentialing Center (ANCC) offers the board certification in Nursing Informatics (NI-BC), which requires a BSN, an active RN license, and specific practice or academic experience in informatics. Organizations like the American Medical Informatics Association (AMIA) and the Healthcare Information and Management Systems Society (HIMSS) provide key resources and networking opportunities for professionals in the field. - A common frustration among frontline nurses is the poor usability of IT systems, including overly complex and difficult-to-navigate Electronic Health Records (EHRs). One UCHealth Epic EHR optimization project reduced documentation time for acute care nurses by 18 minutes per 12-hour shift after a task force of nurses and IT analysts redesigned flowsheets to hide irrelevant information. - The 21st Century Cures Act, implemented through rules from the Office of the National Coordinator for Health IT (ONC) and Centers for Medicare & Medicaid Services (CMS), mandates increased interoperability and prohibits "information blocking". This requires healthcare providers to facilitate patient access to their electronic health information via third-party applications. - Interoperability standards like HL7 Fast Healthcare Interoperability Resources (FHIR) are critical for exchanging electronic health data. FHIR uses a modern, web-based approach with building blocks called "resources" to allow different health IT systems to share information seamlessly. - Data science skills are becoming increasingly important in nursing informatics, utilizing techniques like machine learning and predictive analytics to identify trends in large datasets, which can improve patient outcomes and operational efficiency. Foundational knowledge in programming languages such as Python or R, along with statistical analysis, are key technical competencies. - Involving nurses in the early stages of health IT design is crucial, yet they are often only brought in during later testing phases. This can result in technology that doesn't align well with clinical workflows, leading to inefficiencies and workarounds. - AI is being used to reduce administrative burdens on nurses by automating tasks like documentation, scheduling, and transcribing patient interactions, freeing up more time for direct patient care. According to one report, 86% of health system-affiliated hospitals in the U.S. used predictive AI technology in the last year.
